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The previous algorithm describes the first attempt to approximate F 0 in the data stream by Flajolet and Martin. Their algorithm picks a random hash function which they assume to uniformly distribute the hash values in hash space. Bar-Yossef et al. in [10] introduced k-minimum value algorithm for determining number of distinct elements in data ...
The first is an example of processing a data stream using a continuous SQL query (a query that executes forever processing arriving data based on timestamps and window duration). This code fragment illustrates a JOIN of two data streams, one for stock orders, and one for the resulting stock trades.
Flow-based programming defines applications using the metaphor of a "data factory". It views an application not as a single, sequential process, which starts at a point in time, and then does one thing at a time until it is finished, but as a network of asynchronous processes communicating by means of streams of structured data chunks, called "information packets" (IPs).
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POGOL, an otherwise conventional data-processing language developed at NSA, compiled large-scale applications composed of multiple file-to-file operations, e.g. merge, select, summarize, or transform, into efficient code that eliminated the creation of or writing to intermediate files to the greatest extent possible. [11]
The batch and streaming sides each require a different code base that must be maintained and kept in sync so that processed data produces the same result from both paths. Yet attempting to abstract the code bases into a single framework puts many of the specialized tools in the batch and real-time ecosystems out of reach. [13]
Data Stream Mining (also known as stream learning) is the process of extracting knowledge structures from continuous, rapid data records. A data stream is an ordered sequence of instances that in many applications of data stream mining can be read only once or a small number of times using limited computing and storage capabilities.
For example, a flow-control-heavy task like code parsing may not easily benefit from SIMD; however, it is theoretically possible to vectorize comparisons and "batch flow" to target maximal cache optimality, though this technique will require more intermediate state. Note: Batch-pipeline systems (example: GPUs or software rasterization pipelines ...